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473 results for “sub-saharan Africa”
Figs 57–62. 57, 60–61. Sphex tomentosus Fabricius, 1787 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 57–62. 57, 60–61. Sphex tomentosus Fabricius, 1787, ♂. 58. S. torridus F. Smith, 1873, habitus of ♀. 59. S. voeltzkowii Kohl, 1909, habitus of ♂. 57. Habitus. 60. Dorsal view of genitalia. 61. Lateral view of penis valvae. 62. Geographic distribution of S. voeltzkowii (red), S. caeruleanus Drury, 1773 (blue), S. mweruensis (Arnold, 1947) (yellow) and S. hades sp. nov. (purple).
Figs 49–56. 49. Sphex fumicatus Christ, 1791 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 49–56. 49. Sphex fumicatus Christ, 1791, habitus of ♀. 50. S. lanatus Mocsáry, 1883, habitus of ♀. 51. S. rufinervis Pérez, 1895, habitus of ♂. 52. S. taschenbergi Magretti, 1884, habitus of ♀. 53. S. fumicatus, frontal view of ♀. 54. S. taschenbergi, frontal view of ♀. 55. Geographic distribution of S. lanatus (red), S. rufinervis (blue) and S. taschenbergi (yellow). 56. Geographic distribution of S. tomentosus Fabricius, 1787 (red) and S. torridus F. Smith, 1873 (blue).
Figs 119–123. 119–120 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 119–123. 119–120. Habitus of ♂. 121. Dorsal view of male genitalia. 122. Lateral view of male genitalia. 119. Sphex comorensis sp. nov. 120–122. S. malagassus de Saussure, 1890. 123. Geographic distribution of S. comorensis sp. nov. (red), S. malagassus (blue), S. meridionalis (Arnold, 1947) (yellow), S. nefrens sp. nov. (purple) and S. occidentalis sp. nov. (green).
Figs 33–40. 33–37 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 33–40. 33–37. Frontal view of faces in females of the umtalicus and meridionalis group. 33. Sphex umtalicus Strand, 1916. 34. S. haemorrhoidalis Fabricius, 1781. 35. S. victoria sp. nov. 36. S. meridionalis (Arnold, 1947). 37. S. nefrens sp. nov. 38. S. cinerascens Dahlbom, 1843 habitus of ♂. 39–40. S. paulinierii Guérin-Méneville, 1843, ♂. 39. Habitus. 40. Mesosomal side (anterior = left).
Figs 41–48. 41–42, 45 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 41–48. 41–42, 45. Sphex erythrinus (Guiglia, 1939). 43–44, 46. Sphex feijeni nom. nov. 41, 43. Habitus of ♀. 42, 44. Habitus of ♂. 45–46. Male genitalia. 47. Geographic distribution of S. cinerascens Dahlbom, 1843 (red), S. paulinierii Guérin-Méneville, 1843 (blue), S. erythrinus (yellow) and S. feijeni nom. nov. (purple). 48. Geographic distribution of S. fumicatus Christ, 1791.
Figs 25–32. 25–26 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 25–32. 25–26. Ventral and lateral view of sternum VIII and genitalia in male of Sphex abyssinicus (Arnold, 1928). 27–28. Frontal view of free clypeal margin in males. 29–32. Dorsal view of fore- and hindwing in males and females of the gaullei group. 27. S. umtalicus Strand, 1916. 28. S. decipiens Kohl, 1895. 29. S. jansei Cameron, 1910, ♀. 30. S. jansei, ♂. 31. S. gaullei Berland, 1927, ♀. 32. S. gaullei, ♂.
Figs 19–24. 19–20 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 19–24. 19–20. Ventral view of the two apical sterna in males of the bohemanni group. 21, 23. Dorsal view of apical third of penis valvae in males of the bohemanni group. 22, 24. Lateral view of penis valvae. 19. Sphex bohemanni Dahlbom, 1845. 20. S. abbotti abbotti W. Fox, 1891. 21– 22. S. stadelmanni stadelmanni Kohl, 1895. 23–24. S. schoutedeni s. lat.
Figs 106–112. 106–107. Sphex jansei Cameron, 1910. 108. S in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 106–112. 106–107. Sphex jansei Cameron, 1910. 108. S. schmideggeri sp. nov. 109. S. pseudosatanas sp. nov. 110–111. S. rufoclypeatus sp. nov. 106, 108–110. Habitus of ♀. 107, 111. Habitus of ♂. 112. Geographic distribution of S. decipiens Kohl, 1895 (red), S. pruinosus Germar, 1817 (blue), S. gaullei Berland, 1927 (yellow), S. jansei (purple) and S. schmideggeri sp. nov. (green).
Fig. 130 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Fig. 130. Bayesian inference phylogenetic tree based on available sequence data of CO1, EF-1α and LWR, with the posterior probability shown at each node. For the Afrotropical species treated in our study, the genus name has been omitted.
Figs 13–18 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 13–18. Faces of females in frontal view. 13. Sphex nigrohirtus Kohl, 1895. 14. Sphex abbotti nivarius subsp. nov. 15. S. abbotti abbotti W. Fox, 1891. 16. S. stadelmanni stadelmanni Kohl, 1895. 17. S. abyssinicus (Arnold, 1928). 18. S. schoutedeni malawicus subsp. nov.
Figs 1–6. 1–3 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 1–6. 1–3. Dorsal view of propodeal setae in different orientation. 4. Lateral view of thoracic dorsum. 5–6. Lateral view of scutellum and metanotum. 1. Sphex pseudopraedator sp. nov., ♀. 2. S. jansei Cameron, 1910, ♂. 3–4. S. umtalicus Strand, 1916, ♀. 5. S. rufoclypeatus sp. nov., ♀. 6. S. gaullei Berland, 1927, ♀.
Figs 7–12. 7–8 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figs 7–12. 7–8. Dorsal view of scutellum and metanotum. 9–10. Lateral view of upper metapleural area (anterior = left). 11. Dorsal view of petiole. 12. Basitarsal rake. 7. Sphex tomentosus Fabricius, 1787, ♂. 8. S. meridionalis (Arnold, 1947), ♀. 9. S. torridus F. Smith, 1873, ♂. 10. S. nigrohirtus Kohl, 1895, ♀. 11. S. haemorrhoidalis Fabricius, 1781, ♀. 12. S. decipiens Kohl, 1895, ♀. α: defined as petiole length; β: defined as length of outer side of tarsomere I; arrow: antepenultimate spine.
Planning universal accessibility to public healthcare in sub-Saharan Africa
<p>Replication code and data for the the paper "Planning universal accessibility to public healthcare in sub-Saharan Africa". </p> <p>The tarball contains computer code (in R and javascript) and input data to replicate or update the analysis and the figures, and the result data of baseline and sensitivity analysis model runs. Powerful (or cloud, e.g. Google Earth Engine, RStudio Cloud, or Google Colab) computing facilities are recommended for a successful replication.</p>
Figure 93–99. 93, 96 in The wasp genus Sphex in Sub-Saharan Africa (Hymenoptera: Sphecidae)
Figure 93–99. 93, 96. Habitus of ♀. 94–95, 97. Habitus of ♂. 93. Sphex pseudopraedator sp. nov. 94. S. schoutedeni schoutedeni Kohl, 1913. 95. S. stadelmanni stadelmanni Kohl, 1895. 96– 97. S. stadelmanni rufus subsp. nov. 98. Geographic distribution of S. pseudopraedator sp. nov. 99. Geographic distribution of S. schoutedeni schoutedeni (red); S. schoutedeni malawicus subsp. nov. (blue); S. stadelmanni stadelmanni (yellow); S. stadelmanni rufus subsp. nov. (purple).
Multi-Annual Indicative Programmes EU for Sub-Saharan Africa (2014-2021-2027)
<p>This dataset is the result of the analysis of all 44 Multi-Annual Indicative Programmes (MIPs) for the period 2021-2027 of the European Union (EU) for Sub-Saharan Africa in which the EU implements its bilateral programmes.</p> <p>In addition, hitherto, 5 of the 29 National Indicative Programmes (NIPs) for the period 2014-2020, which are the of the predecessors of the MIPs, have been analyzed. In the following months, an updated dataset including the remaining 24 NIPs(2014-2020) can be expected.</p> <p>The documents have been examined from a financial perspective, and provide the following information:</p> <p><strong>European Development Finance Institutions (EDFI) Activity:</strong></p> <p>- Which European (and International) Development Finance Institutions are mentioned (/active) in which Sub-Saharan African (SSA) country.</p> <p><strong>Member State (MS) Activity:</strong></p> <p>- Which European Member States (MS) are mentioned (/ active) in which SSA country.</p> <p>- Which other international actors are mentioned in which SSA country.</p> <p><strong>Budgets:</strong></p> <p>- How much funding does each SSA country receive of the NDICI (2021) or EDF (2014) budget.</p> <p>- How much funding is attributed to which financial instrument; specifically Team Europa Initiatives (TEIs) and the External Action Guarantee (EAG).</p> <p>- Which concrete other numbers have been mentioned per actor (selected EU member states and development banks).</p> <p><strong>Budgets comparison</strong></p> <p>- A preliminary comparison, however not the same time period (!). The budgets in the 2021-2027 MIPs mentioned are those for the first period and are for the timer frame 2021-202<strong>4</strong>. The budgets in the 2014-2020 NIPs are for the full term.</p> <p><strong>Instruments</strong>:</p> <p>- Which (financial) instruments are mentioned (/will be used) in which SSA country; specifically micro loans, blending, guarantees, technical assistance, Public-Private Partnerships (PPPs), and others.</p> <p>- For the 2021-2027 MIPs: Does the SSA country receive a Team Europe Initiative? If yes, how many, and which actors are involved in the TEI.</p> <p><strong>Policy Priorities</strong></p> <p>- What are the three EU policy priorities per SSA country.</p>
Fig. 2 in First records of the Chrysis leachii group from sub-Saharan Africa, with description of a new species (Hymenoptera, Chrysididae)
Fig. 2. Chrysis rasnitsyni sp. n., female: A – habitus, lateral view; B – head, frontal view; C – metasoma, postero-lateral view; D – metasoma, ventral view. Scale bars 1.0 mm.
Maize management and yield of smallholder farmers in Sub-Saharan Africa between 2016 and 2022
<p>Yield and management practices data were collected from smallholders’ maize fields from 2016 to 2022. All fields corresponded to maize grown in pure stands (no intercropping). Data were collected from five maize producing regions in Sub-Saharan Africa: (i) north-central Nigeria (<em>n</em> = 115), (ii) Rwanda and Burundi (<em>n</em> = 2720), (iii) central Zambia (<em>n</em> = 861)<strong>,</strong> (iv) southwest Tanzania (<em>n</em> = 3710), and (v) eastern Uganda and western Kenya (<em>n</em> = 7367). Data were collected by One Acre Fund (https://oneacrefund.org/), an NGO that provides smallholder farmers access to agricultural training, credit, crop insurance services, and farming supplies. About half of the fields in the database comprised farmers who subscribed to the One Acre Fund program and the other half farmers who did not. </p> <p>Maize grain yield, plant density, and row spacing were measured in two randomly placed boxes of 36 square meters at harvest, avoiding field edges. Field geolocation was recorded in 70% of the observations. When missing, the field geolocation was defined based on the nearby town (21%) or associated district (9%) location for the purpose of retrieving climate data. Management practices associated with each field were reported by farmers, including sowing and harvest dates, cultivar name, fertilizer inputs (types and total quantities for both organic and inorganic), fertilization method, liming, weeding, and pesticides (mainly insecticides to control fall armyworms). Farmers also reported the incidence of adversities (such as pests, diseases, Striga witchweed, hail, and excess water). Field size was reported by farmers and, in those cases in which farmers could not provide an accurate measure of their field size, or there was a strong indication of mistakes (e.g., nutrient fertilizer rates out of range), One Acre Fund personnel took in-situ measurements to determine field size. Input rates per hectare were calculated as the ratio of the farmer-reported input amount and field size. Data were subjected to quality control to remove unlikely values. Maize yield outliers were detected with a Bonferroni Outlier Test. Observations with plant densities and fertilizer rates higher than four standard deviations from the mean were excluded as well as those without geolocation, no N or P data, and atypical sowing dates. After quality control, the database contains a total of 14,773 field observations.</p> <p>Inorganic fertilizer rates were converted to nutrient rates (in elemental nutrients) following typical fertilizer nutrient contents. Organic fertilizers were encoded separately in two binary variables and one continuous variable, indicating whether compost was used, if that compost contained manure, and compost application rate. Likewise, cultivars were classified into hybrids or open pollination varieties (OPVs), which included local varieties, retained seed, and improved OPVs. For hybrids, we retrieved the associated crop cycle maturity (short, medium, and long), disease tolerance traits, and year of release from companies’ seed catalogs. Reported incidence of diseases and insect pests (e.g., anthracnose, aphids, blight, cutworms, drought, fall armyworm, stemborer, termites, and stalk or kernel rot) were simplified to two binary variables indicating whether the crop was affected by pests and/or diseases. Infestation by parasitic witchweeds (Striga hermonthica and S. asiatica) was considered as a separate variable. Fertilization methods were also simplified to whether the fertilizer was applied inside a hole or broadcasted in the surface. Number of weeding operations was simplified to zero, one or two or more weeding per season. Sowing dates were expressed as a deviation from the estimated average sowing date for each climate zone-season combination. Fields were grouped based on their location using the climate zone scheme developed by the Global Yield Gap Atlas Project (www.yieldgap.org). Isolated observations (more than three standard deviations from the median distance across sites within the climate zone) were excluded from their group. In the case of climate zones with two maize seasons, each crop season was considered as a separate group. Field elevation was retrieved from the Amazon Web Services Terrain Tiles. Total precipitation during the growing season, as well as for early, flowering, and grain filling phases, was retrieved from CHIRP. For observations with field-level coordinates data, root-zone plant-available water-holding capacity was retrieved from the World Soil Information database, and soil clay content, pH, organic carbon, and effective cation exchange capacity from iSDA. Lastly, the topography wetness index (TWI) was calculated from the elevation data. </p> <p>Table 1. List of survey-derived variables.</p> <div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Type</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>plant_date_dev</td> <td>discrete</td> <td>days</td> <td>sowing date deviation from cluster average</td> </tr> <tr> <td>pl_m2</td> <td>continuous</td> <td># m2</td> <td>plant density (plants per area)</td> </tr> <tr> <td>row_spacing</td> <td>continuous</td> <td>cm</td> <td>distance between rows</td> </tr> <tr> <td>hybrid</td> <td>binary</td> <td>-</td> <td>Was a commercial hybrid seed used?</td> </tr> <tr> <td>hyb_mat</td> <td>ordinal</td> <td>-</td> <td>hybrid maturity (early, medium, late)</td> </tr> <tr> <td>hyb_yor</td> <td>continuous</td> <td>-</td> <td>Year of release of the cultivar</td> </tr> <tr> <td>hyb_tol_mln</td> <td>binary</td> <td>-</td> <td>Tolerance to maize lethal necrosis</td> </tr> <tr> <td>hyb_tol_msv</td> <td>binary</td> <td>-</td> <td>Tolerance to maize streak virus</td> </tr> <tr> <td>hyb_tol_gls</td> <td>binary</td> <td>-</td> <td>Tolerance to gray leaf spot</td> </tr> <tr> <td>hyb_tol_nclb</td> <td>binary</td> <td>-</td> <td>Tolerance to northern corn leaf blight</td> </tr> <tr> <td>hyb_tol_rust</td> <td>binary</td> <td>-</td> <td>Tolerance to rust</td> </tr> <tr> <td>hyb_tol_ear_rot</td> <td>binary</td> <td>-</td> <td>Tolerance to ear rot</td> </tr> <tr> <td>N_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>N fertilization rate</td> </tr> <tr> <td>P_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>P fertilization rate</td> </tr> <tr> <td>K_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>K fertilization rate</td> </tr> <tr> <td>compost</td> <td>binary</td> <td>-</td> <td>Was compost applied?</td> </tr> <tr> <td>comp_t_ha</td> <td>continuous</td> <td>t/ha</td> <td>compost rate</td> </tr> <tr> <td>manure</td> <td>binary</td> <td>-</td> <td>Did the compost contain manure?</td> </tr> <tr> <td>fert_in_hole</td> <td>binary</td> <td>-</td> <td>Was the fertilizer applied in a hole?</td> </tr> <tr> <td>lime_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>lime rate</td> </tr> <tr> <td>weeding</td> <td>discrete</td> <td>#</td> <td>number of times the plot was weeded</td> </tr> <tr> <td>pesticide</td> <td>binary</td> <td>-</td> <td>Was any pesticide applied?</td> </tr> <tr> <td>disease</td> <td>binary</td> <td>-</td> <td>Was yield affected by diseases?</td> </tr> <tr> <td>pest</td> <td>binary</td> <td>-</td> <td>Was yield affected by pests?</td> </tr> <tr> <td>striga</td> <td>binary</td> <td>-</td> <td>Was yield affected by the Striga weed?</td> </tr> <tr> <td>water_excess</td> <td>binary</td> <td>-</td> <td>Was yield affected by water excess (heavy rain or flooding)?</td> </tr> </tbody> </table> <p> </p> <p><strong>Table 2. List of environmental variables. </strong></p> <div> </div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Spatial resolution</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>GDD</td> <td>°C days</td> <td>30 arc-sec (1km)</td> <td>Growing degree days</td> <td>www.worldclim.org</td> </tr> <tr> <td>AI</td> <td>unitless</td> <td>30 arc-sec (1km)</td> <td>Aridity Index (annual precipitation over potential evapotranspiration)</td> <td>www.worldclim.org</td> </tr> <tr> <td>TS</td> <td>°C</td> <td>30 arc-sec (1km)</td> <td>Temperature seasonality</td> <td>www.worldclim.org</td> </tr> <tr> <td>season_prec</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Total rainfall during the maize season (10% of planting to 50% of the harvest)</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_1</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the first third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_2</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the second third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_3</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the last third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>elev</td> <td>m.a.s.l.</td> <td>75 meters</td> <td>Elevation (altitude) above sea level</td> <td>registry.opend26ata.aws/terrain-tiles</td> </tr> <tr> <td>soil_rzpawhc</td> <td>mm</td> <td>1 km</td> <td>Root zone plant-available water holding capacity</td> <td>www.isric.org</td> </tr> <tr> <td>soil_clay</td> <td>%</td> <td>30 meters</td> <td>Clay content at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_pH</td> <td>-</td> <td>30 meters</td> <td>pH (H2O) at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_orgC</td> <td>g/kg</td> <td>30 meters</td> <td>Organic carbon at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_ECEC</td> <td>cmolc/kg</td> <td>30 meters</td> <td>Effective cation exchange capacity at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>twi</td> <td>unitless</td> <td>75 meters</td> <td>Topographic Wetness Index</td> <td>calculated from elevation</td> </tr> </tbody> </table> </div> </div> </div>
Fig. 3 in Characterization of sounds in maize produced by internally feeding insects: investigations to develop inexpensive devices for detection of Prostephanus truncatus (Coleoptera: Bostrichidae) and Sitophilus zeamais (Coleoptera: Curculionidae) in small-scale storage facilities in sub-Saharan Africa
Fig. 3. Effects of distance on detectability of larval sound impulses. Horizontal axis indicates the mean distance between the larval pouch and the sensor; vertical axis indicates the log10-transformed mean rate of impulses detected at that distance. Bars indicate the standard error of mean transformed rate.
Fig. 1 in Characterization of sounds in maize produced by internally feeding insects: investigations to develop inexpensive devices for detection of Prostephanus truncatus (Coleoptera: Bostrichidae) and Sitophilus zeamais (Coleoptera: Curculionidae) in small-scale storage facilities in sub-Saharan Africa
Fig. 1. Spectral profiles of 4 distinctive types of larval sound impulses detected in cracked corn: HaNb, solid line; Ma, dashed line, Ha, dash-dot-dotted line, and La, dotted line. Horizontal axis indicates frequency in kHz and vertical axis indicates relative spectrum amplitude in dB.
Fig. 2 in Characterization of sounds in maize produced by internally feeding insects: investigations to develop inexpensive devices for detection of Prostephanus truncatus (Coleoptera: Bostrichidae) and Sitophilus zeamais (Coleoptera: Curculionidae) in small-scale storage facilities in sub-Saharan Africa
Fig. 2. Oscillogram of sound impulses recorded 10 cm from pouch containing Sitophilus oryzae larvae. Examples of 3 types of larval sound impulse occur during the 1 s period, and one example each of type (Ha, La, and HaNb) is marked above the impulse. Horizontal axis indicates time in seconds and vertical axis indicates relative signal amplitude.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.